the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improving the Gaussianity of Radar Reflectivity Departures between Observations and Simulations by Using the Symmetric Rain Rate
Abstract. Given that the Gaussianity of observation error distribution is the fundamental principle of some data assimilation and machine learning algorithms, the error structure of radar reflectivity becomes increasingly important with the development of high resolution forecasts and nowcasts of convective systems. This study examines the error distribution of radar reflectivity and discusses what give rise to the non-Gaussian error distribution by using 6 month observations minus backgrounds (OmBs) of composites of vertical maximum reflectivity (CVMRs) in mountainous and hilly areas. By following the symmetric error model in all-sky satellite radiance assimilation, we unveil the error structure of CVMRs as a function of symmetric rain rates, which is the average of observed and simulated rain rates. Unlike satellite radiance, the error structure of CVMRs shows a sharper slope in light precipitations than moderate precipitations. Thus, a three-piecewise fitting function is more suitable for CVMRs. The probability density functions of OmBs normalized by symmetric rain rates become more Gaussian in comparison with the probability density function normalized by the whole samples. Moreover, the possibility of using third-party predictor to construct the symmetric error model are also discussed in this study. The Gaussianity of OmBs can be further improved by using a more accurate precipitation observations. According to the Jensen-Shannon divergence, a more linear predictor, the logarithmic transformation of rain rate, can provide the most Gaussian error distribution in comparison with other predictors.
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RC1: 'Comment on amt-2024-15', Anonymous Referee #1, 20 Mar 2024
This manuscript proposes an approach to handle the non-Gaussian error distribution of reflectivity OmBs (dBZ), which adopts the idea of the symmetric error model in all-sky radiance data assimilation. This work demonstrates that the symmetric error model built by the rainrate predictor, can improve the Gaussianity of OmB distribution, by using six-month composite reflectivity data and simulated products. Moreover, the reflectivity OmBs present a more complicated error model that can be fitted by a three-piecewise function, compared to the satellite radiances, since the radar reflectivity is often discontinues. This manuscript is well structured and could be a valuable contribution to the radar and data assimilation communities. I have several comments as below. I’d like to recommend minor revision to this manuscript.
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- This work compares two OmB data, the maximum composite and the reflectivity at 1Â km. Results show that two OmB data have similar features, such as horizontal distributions and PDFs. The rainrate derived from the reflectivity at 3 km is then used to build the symmetric error model of the maximum composite. However, the maximum composite and reflectivity at 1 km and 3 km, respectively, could be different. The correlations between the derived rainrate and the maximum composite (or/and the reflectivity at 1Â km) are needed d to clarify the potential inconsistent usages of data.
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- There various types of data used in this work, especially with different horizontal resolutions (e.g., the 5-km CMPAS and 3-km WRF products). Thus how the interpolation performed to deal with the inconsistent resolutions needs clarification. It is possible that the interpolation increases the OmB variances. The authors emphasize that all data are collected in mountainous areas. Does the interpolation consider the effects of terrain? Moreover, how about the resolution of CVMR and CAPPI used in this study?
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- The reflectivity OmBs highly depend on the forward operator. More elucidations about the forward operator are needed, in order to clarify how the OmB is derived.
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- Figure 8, it is interesting that the logarithmic rainrates (Figure 8c) has a different distribution than the rainrates (Figure 8a) and CMPAS rainrates (Figure 8b), for the magnitudes of rainrates larger than 10 mm h-1. Why the three-piecewise fitting function for the logarithmic rainrates does not capture the decrease trend for those magnitudes larger than 10 mm h-1?
Citation: https://doi.org/10.5194/amt-2024-15-RC1 - AC1: 'Reply on RC1', Yudong Gao, 27 Mar 2024
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RC2: 'Comment on amt-2024-15', Anonymous Referee #2, 17 Apr 2024
General comments:
The paper uses a symmetric rain rate to define the radar reflectivity error in the assimilation algorithm based on the symmetric rain rate referring to the symmetric error model in satellite all-sky assimilation. The paper is well-structured but still, there are many ambiguous sentences in the paper which need to be rewritten/clarified.
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